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Cognitive Systems Research

Quantum projections on conceptual subspaces

Alejandro Martínez-Mingo, Guillermo Jorge‐Botana, José Ángel Martínez‐Huertas, Ricardo Olmos Albacete

Published Jul 27, 2023• 4 citations
Publisher
DOI
10.1016/j.cogsys.2023.101154

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In alignment with the distributional hypothesis of language, the work “Quantum Projections on Conceptual Subspaces” (Martínez-Mingo A, Jorge-Botana G, Martinez-Huertas JÁ, et al. Quantum projections on conceptual subspaces. Cogn Syst Res 2023; 82: 101154) proposed a methodology for generating conceptual subspaces from textual information based on previous work (Martinez-Mingo A, Jorge-Botana G and Olmos R. Quantum approach for similarity evaluation in LSA vector space models. 2020). These subspaces enable the utilization of the quantum model of similarity put forth by Pothos and Busemeyer (Pothos E, Busemeyer J. A quantum probability explanation for violations of symmetry in similarity judgments. In Proceedings of the annual meeting of the cognitive science society, 2011, Vol. 33, No. 33), allowing for the empirical examination of the violations of assumptions concerning symmetry and triangular inequality (Tversky A. Features of similarity. Psychol Rev 1977; 84: 327–352; Yearsley JM, Barque-Duran A, Scerrati E, et al. The triangle inequality constraint in similarity judgments. Prog Biophys Mol Biol 2017; 130: 26–32), as well as the diagnosticity effect (Tversky A. Features of similarity. Psychol Rev 1977; 84: 327–352; Yearsley JM, Pothos EM, Barque-Duran A, et al. Context effects in similarity judgments. J Exp Psychol Gen 2022; 151: 711–717), within a data-driven environment. These psychological biases, deeply studied by authors such as Tversky and Kahneman, inform us about the limitations of modeling psychological similarity measures using tools from classical geometry. This commentary aims to offer methodological clarifications, discuss theoretical and practical implications, and speculate on future directions in this field of research. Concretely, it aims to propose the use of different contours (conceptual or contextual) to generate the subspaces, which lead to subspaces of terms or contexts. Once these contours are defined, a differentiation is proposed between Aggregated Terms Subspaces (ATSs), Aggregated Contexts Subspaces (ACSs), and Aggregated Features Subspaces (AFSs) depending on whether we define the subspaces by grouping the terms or contexts within the contour, or from the latent dimensions of the semantic space obtained in the contour window. Finally, new data is provided on the violation of the triangular inequality assumption through the application of the quantum similarity model to ATSs.

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In alignment with the distributional hypothesis of language, the work “Quantum Projections on Conceptual Subspaces” (Martínez-Mingo A, Jorge-Botana G, Martinez-Huertas JÁ, et al. Quantum projections on conceptual subspaces. Cogn Syst Res 2023; 82: 101154) proposed a methodology for generating conceptual subspaces from textual information based on previous work (Martinez-Mingo A, Jorge-Botana G and Olmos R. Quantum approach for similarity evaluation in LSA vector space models. 2020). These subspaces enable the utilization of the quantum model of similarity put forth by Pothos and Busemeyer (Pothos E, Busemeyer J. A quantum probability explanation for violations of symmetry in similarity judgments. In Proceedings of the annual meeting of the cognitive science society, 2011, Vol. 33, No. 33), allowing for the empirical examination of the violations of assumptions concerning symmetry and triangular inequality (Tversky A. Features of similarity. Psychol Rev 1977; 84: 327–352; Yearsley JM, Barque-Duran A, Scerrati E, et al. The triangle inequality constraint in similarity judgments. Prog Biophys Mol Biol 2017; 130: 26–32), as well as the diagnosticity effect (Tversky A. Features of similarity. Psychol Rev 1977; 84: 327–352; Yearsley JM, Pothos EM, Barque-Duran A, et al. Context effects in similarity judgments. J Exp Psychol Gen 2022; 151: 711–717), within a data-driven environment. These psychological biases, deeply studied by authors such as Tversky and Kahneman, inform us about the limitations of modeling psychological similarity measures using tools from classical geometry. This commentary aims to offer methodological clarifications, discuss theoretical and practical implications, and speculate on future directions in this field of research. Concretely, it aims to propose the use of different contours (conceptual or contextual) to generate the subspaces, which lead to subspaces of terms or contexts. Once these contours are defined, a differentiation is proposed between Aggregated Terms Subspaces (ATSs), Aggregated Contexts Subspaces (ACSs), and Aggregated Features Subspaces (AFSs) depending on whether we define the subspaces by grouping the terms or contexts within the contour, or from the latent dimensions of the semantic space obtained in the contour window. Finally, new data is provided on the violation of the triangular inequality assumption through the application of the quantum similarity model to ATSs.
This publication develops the geometric interpretation of the Resonance-Based Subspace Dynamics (RBS-D) framework, extending the conceptual foundation introduced in Publication 01. It establishes a structural description in which physical reality is modeled as a continuous subspace, where particles and observable properties emerge from stable resonance configurations and relational mode structures. This publication (No. 02 / 24) defines the geometric interpretation layer of the RBS-D framework and provides the transition from conceptual hypothesis to structurally interpretable subspace geometry. Core Contributions: • Introduction of a structural vision for a unified physical description based on resonance dynamics • Definition of physical entities as:Stable, phase-coherent mode configurations within a continuous subspace • Conceptual shift from particles to:Relational, geometry-dependent structures • Interpretation of physical properties as:– emergent from internal structure– dependent on phase relations and coupling geometry • Structural hypothesis:Mathematical state spaces represent projections of deeper relational subspace structures Motivation and Conceptual Position: Modern physics provides highly accurate predictions, yet often relies on abstract mathematical frameworks with limited direct structural interpretation. Phenomena such as:• high-dimensional quantum states• nonlocal correlations• wave-particle duality suggest that an underlying structural layer may exist. RBS-D introduces a framework that:• preserves empirical validity• while offering a more physically interpretable structural description Core Principles: • Physical reality is continuous at the foundational level • Stable physical entities correspond to resonance states with phase coherence • Observable properties emerge from internal geometric and relational structure • Mathematical representations correspond to projections of deeper subspace relations Within the RBS-D framework, physical systems are described in terms of: • continuous subspace structures• phase-coherent mode configurations• relational geometry between interacting components• resonance-based stability conditions RBS-D CONTENT MAP (Series Navigation) • Core Focus:Conceptual and geometric vision of subspace-based physical description • Key Contribution:Transition from conceptual hypothesis (P01) to geometric interpretation • Extends:→ Publication 01 (Foundational Concept and Structural Framework) • Leads to:→ Publication 03 (Mathematical Operator Formalization)→ Publication 04 (Stability Theory of Resonance Structures) • Application Context:Conceptual physics, geometric modeling, interpretation of quantum and field structures Recommended Entry Path: → Publication 01 (Conceptual Foundation)→ Publication 03 (Mathematical Formalization)→ Publication 04 (Stability Theory) Series Context: This publication is part of the 24-part framework"Resonance-Based Subspace Dynamics (RBS-D)". The series develops a unified structural approach across: • theoretical physics• applied mathematics• nonlinear dynamics• plasma and fluid systems• astrophysical structures The framework progresses from conceptual foundations (P01–P03)to geometric and stability analysis (P04–P07),and further toward applied resonance engineering (P15–P24). Scientific Position: RBS-D does not replace established physical theories, but provides a structural interpretation that connects them through resonance-based dynamics and relational geometry. Abstract (English) This publication develops the geometric interpretation of the Resonance-Based Subspace Dynamics (RBS-D) framework, in which physical reality is described as a continuous subspace structured by resonance and phase relations. Physical entities are interpreted as stable, phase-coherent mode configurations, while observable properties emerge from internal geometric and relational structure. Mathematical state spaces are understood as projections of deeper subspace dynamics. This work establishes the transition from conceptual foundation to geometric interpretation and provides the basis for subsequent mathematical formalization and stability analysis within the RBS-D series.
A precise physical description and understanding of the classical dual content of quantum theory is necessary in many disciplines today: from concepts and interpretation to quantum technologies and computation. In this paper we investigate Quantum Entanglement with the new approach APL Quantum 2, 016104 (2025) on dual Classicalization. Thus, the results of this paper are twofold: Entanglement and Classicalization and the relationship between them. Classicalization truly occurs only under the action of the Metaplectic group Mp(n) (Minimal Representation group, double covering of the Symplectic group). Some of the results of this paper involves the computation and analysis of the entanglement for different types of coherent (coset and non coset) states and topologies: in the circle and the cylinder. We project the entangled wave functions onto the even (+) and odd (-) irreducible Hilbert Mp(n) subspaces, and compute their square norms: Entanglement Probabilities P++, P--, P+-, (eg in the same or in the different subspaces), and the Total sum of them, and more. These theoretical and conceptual results can be of experimental and practical real-world interest.